scholarly journals Hybrid Ontology for Semantic Information Retrieval Model Using Keyword Matching Indexing System

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
K. R. Uthayan ◽  
G. S. Anandha Mala
Keyword(s):  
Information Retrieval ◽  
Keyword Search ◽  
Predicate Logic ◽  
Relevant Information ◽  
Semantic Matching ◽  
Matching Process ◽  
Executive Process ◽  
Indexing System ◽  
Primary Order ◽  
Information Domain

Ontology is the process of growth and elucidation of concepts of an information domain being common for a group of users. Establishing ontology into information retrieval is a normal method to develop searching effects of relevant information users require. Keywords matching process with historical or information domain is significant in recent calculations for assisting the best match for specific input queries. This research presents a better querying mechanism for information retrieval which integrates the ontology queries with keyword search. The ontology-based query is changed into a primary order to predicate logic uncertainty which is used for routing the query to the appropriate servers. Matching algorithms characterize warm area of researches in computer science and artificial intelligence. In text matching, it is more dependable to study semantics model and query for conditions of semantic matching. This research develops the semantic matching results between input queries and information in ontology field. The contributed algorithm is a hybrid method that is based on matching extracted instances from the queries and information field. The queries and information domain is focused on semantic matching, to discover the best match and to progress the executive process. In conclusion, the hybrid ontology in semantic web is sufficient to retrieve the documents when compared to standard ontology.

GeTFIRST: ontology-based keyword search towards semantic disambiguation

2015 ◽  
Vol 11 (4) ◽  
pp. 442-467 ◽  
Author(s):  
Hoang-Minh Nguyen ◽  
Hong-Quang Nguyen ◽  
Khoi-Nguyen Tran ◽  
Xuan-Vinh Vo
Keyword(s):  
Information Retrieval ◽  
Information Integration ◽  
Keyword Search ◽  
Classification Tree ◽  
Empirical Evaluation ◽  
Relevant Information ◽  
Ranking Algorithm ◽  
Content Type ◽  
Information Retrieval Methods ◽  
Search Approach

Purpose – This paper aims to improve the semantic-disambiguation capability of an information-retrieval system by taking advantages of a well-crafted classification tree. The unstructured nature and sheer volume of information accessible over networks have made it drastically difficult for users to seek relevant information. Many information-retrieval methods have been developed to address this problem, and keyword-based approach is amongst the most common approach. Such an approach is often inadequate to cope with the conceptualization associated with user needs and contents. This brings about the problem of semantic ambiguation that refers to the disagreement in meaning of terms between involving parties of a communication due to polysemy, leading to increased complexity and lesser accuracy in information integration, migration, retrieval and other related activities. Design/methodology/approach – A novel ontology-based search approach, named GeTFIRST (short for Graph-embedded Tree Fostering Information Retrieval SysTem), is proposed to disambiguate keywords semantically. The contribution is twofold. First, a search strategy is proposed to prune irrelevant concepts for accuracy improvement using our Graph-embedded Tree (GeT)-based ontology. Second, a path-based ranking algorithm is proposed to incorporate and reward the content specificity. Findings – An empirical evaluation was performed on United States Patent And Trademark Office (USPTO) patent datasets to compare our approach with full-text patent search approaches. The results showed that GeTFIRST handled the ambiguous keywords with higher keyword-disambiguation accuracy than traditional search approaches. Originality/value – The search approach of this paper copes with the semantic ambiguation by using our proposed GeT-based ontology and a path-based ranking algorithm.

scholarly journals Information Retrieval on Web: Ontology based Vs Traditional Search Engines

2019 ◽  
Vol 8 (3) ◽  
pp. 901-903
Keyword(s):  
Information Retrieval ◽  
Search Engines ◽  
Keyword Search ◽  
Relevant Information ◽  
The Internet ◽  
Large Pool ◽  
Tremendous Amount ◽  
Advanced Computing ◽  
The Comparative Study ◽  
The Web

Information Retrieval has become the buzzword in the today’s era of advanced computing. The tremendous amount of information is available over the Internet in the form of documents which can either be structured or unstructured. It is really difficult to retrieve relevant information from such large pool. The traditional search engines based on keyword search are unable to give the desired relevant results as they search the web on the basis of the keywords present in the query fired. On contrary the ontology based semantic search engines provide relevant and quick results to the user as the information stored in the semantic web is more meaningful. The paper gives the comparative study of the ontology based search engines with those which are keyword based. Few of both types have been taken and same queries are run on each one of them to analyze the results to compare the precision of the results provided by them by classifying the results as relevant or non-relevant.

scholarly journals Development of Intelligent Information System for Digital Cultural Contents

Mathematics ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 238
Author(s):  
Yuna Hur ◽  
Jaechoon Jo
Keyword(s):  
Information System ◽  
User Satisfaction ◽  
Keyword Search ◽  
Relevant Information ◽  
Matter Content ◽  
System Stability ◽  
Knowledge Network ◽  
Intelligent Information ◽  
Subject Matter Content ◽  
Depth Interviews

A significant amount of digital cultural contents is shared online, but learners do not know where subject matter content is or how to find it. Therefore, there is a need for a service to improve educational quality by effectively providing relevant information in response to searches for content that is useful to learners. This study developed and tested the usability and utility of an intelligent information system that effectively searches and visualizes digital cultural contents. The system collects data on digital cultural contents, automatically classifies them, and creates content triple data to automatically display the results with a 3D timeline, knowledge network map, and keyword relation network map through content search, triple search, and keyword search. We also conducted a survey and in-depth interviews to verify users’ satisfaction with respect to the use and utility of the system. For the experiment, we developed survey questions to measure user satisfaction and conducted in-depth interviews regarding the system’s utility with a total of 65 subjects. The results show that the response for satisfaction with regard to the use and utility was generally “satisfied”. In addition, the system stability was evaluated as “high”.

A distributional semantics-based information retrieval framework for online social networks

2021 ◽  
pp. 1-11
Author(s):  
V.S. Anoop ◽  
P. Deepak ◽  
S. Asharaf
Keyword(s):  
Social Networks ◽  
Information Retrieval ◽  
Online Social Networks ◽  
Latent Dirichlet Allocation ◽  
Relevant Information ◽  
Distributional Semantics ◽  
Scalable Algorithms ◽  
Mobile Platforms ◽  
Cancer Support ◽  
Efficient Extraction

Online social networks are considered to be one of the most disruptive platforms where people communicate with each other on any topic ranging from funny cat videos to cancer support. The widespread diffusion of mobile platforms such as smart-phones causes the number of messages shared in such platforms to grow heavily, thus more intelligent and scalable algorithms are needed for efficient extraction of useful information. This paper proposes a method for retrieving relevant information from social network messages using a distributional semantics-based framework powered by topic modeling. The proposed framework combines the Latent Dirichlet Allocation and distributional representation of phrases (Phrase2Vec) for effective information retrieval from online social networks. Extensive and systematic experiments on messages collected from Twitter (tweets) show this approach outperforms some state-of-the-art approaches in terms of precision and accuracy and better information retrieval is possible using the proposed method.

OP457 A Collaborative Horizon Scanning Alert For Disinvestment And Early Awareness

2020 ◽  
Vol 36 (S1) ◽  
pp. 10-10
Author(s):  
Vigdis Lauvrak ◽  
Kelly Farrah ◽  
Rosmin Esmail ◽  
Anna Lien Espeland ◽  
Elisabet Hafstad ◽  
...  
Keyword(s):  
Information Retrieval ◽  
Interest Group ◽  
Pilot Project ◽  
Relevant Information ◽  
Horizon Scanning ◽  
Google Alerts ◽  
One Year ◽  
The One ◽  
New Research ◽  
Google Search

IntroductionIn 2019, the Norwegian Institute for Public Health and Canadian Agency for Drugs and Technologies in Health (CADTH) received support from HTAi to produce a quarterly current awareness alert for the HTAi Disinvestment and Early Awareness Interest Group in collaboration with the HTAi Information Retrieval Interest Group. The alert focuses on methods and topical issues, and broader forecasts of potentially disruptive technologies that may be of interest to those involved in horizon scanning and disinvestment initiatives in health technology assessment (HTA).MethodsInformation specialists at both agencies developed search strategies for disinvestment and for horizon scanning in PubMed and Google. The template for the alert was based on an e-newsletter developed by the Information Retrieval Interest Group. Information specialists and researchers reviewed the monthly (PubMed) and weekly (Google) search results and selected potentially relevant publications. Additional sources were also identified through regular HTA and horizon scanning work.ResultsAlerts are posted quarterly on the HTAi Interest Group website; members receive an email notice when new alerts are available. While the revised PubMed searches are identifying relevant information, Google alerts have been disappointing, and this search may need to be revised further or dropped. When the one-year pilot project ends, in Fall 2020, interest group members will be surveyed to see if the alerts were useful, and whether they have suggestions for improving them.ConclusionsCollaborating on this alert service reduces duplication of effort between agencies, and makes new research in horizon scanning and disinvestment more accessible to colleagues in other agencies working in these areas.

Open System for Indexing and Retrieving Multimedia Information

2013 ◽  
Author(s):  
Hanene Maghrebi ◽  
Amos David
Keyword(s):  
Information Retrieval ◽  
Multimedia Information ◽  
Open System ◽  
Large Volume ◽  
Retrieval System ◽  
Main Idea ◽  
Relevant Information ◽  
Information Retrieval System ◽  
Multimedia Content ◽  
Nous Proposons

Managing the increasing growth of multimedia content still poses some problems. The challenge is to propose relevant information to the users among the large volume of information available. The main idea that drives our approach is to provide an open information retrieval system, which can adapt its results to several…La gestion de l’information multimédia soulève encore quelques problèmes. Le défi est de pouvoir proposer à l’utilisateur des informations pertinentes parmi la quantité d’information qui ne cesse de s’accroître. Dans cette lignée, nous proposons un système ouvert de recherche d’information capable d’adapter ses résultats aux différents… 

scholarly journals GeoIRIS: Geospatial Information Retrieval and Indexing System—Content Mining, Semantics Modeling, and Complex Queries

2007 ◽  
Vol 45 (4) ◽  
pp. 839-852 ◽  
Author(s):  
Chi-Ren Shyu ◽  
Matt Klaric ◽  
Grant J. Scott ◽  
Adrian S. Barb ◽  
Curt H. Davis ◽  
...  
Keyword(s):  
Information Retrieval ◽  
Geospatial Information ◽  
Complex Queries ◽  
Indexing System ◽  
Content Mining

scholarly journals An Art of Review on Conceptual based Information Retrieval

Webology ◽  
2021 ◽  
Vol 18 (SI02) ◽  
pp. 21-31
Author(s):  
P. Mahalakshmi ◽  
N. Sabiyath Fathima
Keyword(s):  
Information Retrieval ◽  
Keyword Search ◽  
Semantic Representation ◽  
Research Work ◽  
Semantic Search ◽  
Future Research ◽  
Conceptual Information ◽  
Retrieval Systems ◽  
User Query ◽  
Information Retrieval Systems

Basically keywords are used to index and retrieve the documents for the user query in a conventional information retrieval systems. When more than one keywords are used for defining the single concept in the documents and in the queries, inaccurate and incomplete results were produced by keyword based retrieval systems. Additionally, manual interventions are required for determining the relationship between the related keywords in terms of semantics to produce the accurate results which have paved the way for semantic search. Various research work has been carried out on concept based information retrieval to tackle the difficulties that are caused by the conventional keyword search and the semantic search systems. This paper aims at elucidating various representation of text that is responsible for retrieving relevant search results, approaches along with the evaluation that are carried out in conceptual information retrieval, the challenges faced by the existing research to expatiate requirements of future research. In addition, the conceptual information that are extracted from the different sources for utilizing the semantic representation by the existing systems have been discussed.

scholarly journals Keyword Search over the COVID-19 Data

2020 ◽  
Author(s):  
Yenier Torres Izquierdo ◽  
Grettel Monteagudo Garcia ◽  
Melissa Lemos ◽  
Alexandre Novello ◽  
Bruno Novelli ◽  
...  
Keyword(s):  
Information Retrieval ◽  
Relational Databases ◽  
Keyword Search ◽  
Attractive Alternative ◽  
Knowledge Retrieval ◽  
Database Access ◽  
Retrieval Systems ◽  
Information Retrieval Systems

Keyword search is typically associated with information retrieval systems. However, recently, keyword search has been expanded to relational databases and RDF datasets, as an attractive alternative to traditional database access. With this motivation, this paper first introduces a platform for data and knowledge retrieval, called DANKE, concentrating on the keyword search component. It then describes an application that uses DANKE to implement keyword search over two COVID-19 data scenarios.

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